Data Analysis - 1.2

I finished the width/height ratio normalisation, and then I did the t-test to compare the new normal and stress results. There are no significant differences between them, with p-value (two-tail) > 0.05. Although this feature fails to be a good feature, I learnt a lot from the normalisation process. What is more, I think this idea can be used in the future analysis as well, because the differences between letters may also have other impacts on the gestures.

Data Analysis-1.1

After discussing with my supervisor, he suggest me to normalise the width/height ratio for different letters, so that it can eliminate the effect of the generic shape of the letter. Then, I will explore more about the bounding box, to understand it in more details and compare my results with the experimenter's results.

In the further analysis, I need to use the actual written letter to group gestures, instead of the automatic recognition, so I need another field in the Stroke called "written". Since I have already summarised all the recognition errors, I will use the list to correct recognition result for each gesture. 

Data Analysis - 1

I would like to start the analysis from an easy and straightforward feature -- bounding box. First of all, this week I wrote some scripts to abstract bounding box information from processed data (one file for each subject), like the width, height, and width over height ratios. Then, I computed average values for different sessions (Normal and High). I will use t-test to compare two groups of results.

However, there are some issues I want to discuss with the supervisor. The most important one is the letter's shape has significant effect on the bounding box, and I am not sure whether I can compare the result. Additionally, I am not sure how to deal with slant, which the stoke is not vertical to the plane, because slant stroke will change the dimension of bounding box.